Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Python for Finance
  • Table Of Contents Toc
  • Feedback & Rating feedback
Python for Finance

Python for Finance

By : Yuxing Yan
3.9 (22)
Buy this Book
close
close
Python for Finance

Python for Finance

3.9 (22)
By: Yuxing Yan
Buy this Book

Overview of this book

A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
Table of Contents (14 chapters)
close
close
close
Preface
Icon
Preface
Icon
Why Python?
Icon
A programming book written by a finance professor
Icon
Small programs oriented
Icon
Using real-world data
Icon
What this book covers
Icon
What could you achieve after reading this book?
Icon
Who this book is for
Icon
Conventions
Icon
Two ways to use the book
Icon
Reader feedback
Icon
Customer support
Free Chapter
1
1. Introduction and Installation of Python
Icon
1. Introduction and Installation of Python
Icon
Introduction to Python
Icon
Installing Python
Icon
Different versions of Python
Icon
Ways to launch Python
Icon
Quitting Python
Icon
Error messages
Icon
Python language is case sensitive
Icon
Initializing the variable
Icon
Finding the help window
Icon
Finding manuals and tutorials
Icon
Finding the version of Python
Icon
Summary
Icon
Exercises
2
2. Using Python as an Ordinary Calculator
Icon
2. Using Python as an Ordinary Calculator
Icon
Assigning values to variables
Icon
Error messages
Icon
Choosing meaningful names
Icon
Using dir() to find variables and functions
Icon
Basic math operations – addition, subtraction, multiplication, and division
Icon
The power function, floor, and remainder
Icon
Choosing appropriate precision
Icon
Finding out more information about a specific built-in function
Icon
Importing the math module
Icon
A few frequently used functions
Icon
The tuple data type
Icon
Summary
Icon
Exercises
3
3. Using Python as a Financial Calculator
Icon
3. Using Python as a Financial Calculator
Icon
Writing a Python function without saving it
Icon
Default input values for a function
Icon
Indentation is critical in Python
Icon
Checking the existence of our functions
Icon
Defining functions from our Python editor
Icon
Activating our function using the import function
Icon
Debugging a program from a Python editor
Icon
Two ways to call our pv_f() function
Icon
Generating our own module
Icon
Types of comments
Icon
The if() function
Icon
Annuity estimation
Icon
Converting the interest rates
Icon
Continuously compounded interest rate
Icon
A data type – list
Icon
Net present value and the NPV rule
Icon
Defining the payback period and the payback period rule
Icon
Defining IRR and the IRR rule
Icon
Showing certain files in a specific subdirectory
Icon
Using Python as a financial calculator
Icon
Adding our project directory to the path
Icon
Summary
Icon
Exercises
4
4. 13 Lines of Python to Price a Call Option
Icon
4. 13 Lines of Python to Price a Call Option
Icon
Writing a program – the empty shell method
Icon
Writing a program – the comment-all-out method
Icon
Using and debugging other programs
Icon
Summary
Icon
Exercises
5
5. Introduction to Modules
Icon
5. Introduction to Modules
Icon
What is a module?
Icon
Importing a module
Icon
Module dependency
Icon
Summary
Icon
Exercises
6
6. Introduction to NumPy and SciPy
Icon
6. Introduction to NumPy and SciPy
Icon
Installation of NumPy and SciPy
Icon
Launching Python from Anaconda
Icon
Showing all functions in NumPy and SciPy
Icon
More information about a specific function
Icon
Understanding the list data type
Icon
Working with arrays of ones, zeros, and the identity matrix
Icon
Performing array manipulations
Icon
Performing array operations with +, -, *, /
Icon
The x.sum() dot function
Icon
Looping through an array
Icon
Using the help function related to modules
Icon
A list of subpackages for SciPy
Icon
Cumulative standard normal distribution
Icon
Logic relationships related to an array
Icon
Statistic submodule (stats) from SciPy
Icon
Interpolation in SciPy
Icon
Solving linear equations using SciPy
Icon
Generating random numbers with a seed
Icon
Finding a function from an imported module
Icon
Understanding optimization
Icon
Linear regression and Capital Assets Pricing Model (CAPM)
Icon
Retrieving data from an external text file
Icon
Installing NumPy independently
Icon
Understanding the data types
Icon
Summary
Icon
Exercises
7
7. Visual Finance via Matplotlib
Icon
7. Visual Finance via Matplotlib
Icon
Installing matplotlib via ActivePython
Icon
Alternative installation via Anaconda
Icon
Understanding how to use matplotlib
Icon
Understanding simple and compounded interest rates
Icon
Adding texts to our graph
Icon
Working with DuPont identity
Icon
Understanding the Net Present Value (NPV) profile
Icon
Graphical representation of the portfolio diversification effect
Icon
Retrieving historical price data from Yahoo! Finance
Icon
Understanding the time value of money
Icon
Candlesticks representation of IBM's daily price
Icon
IBM's intra-day graphical representations
Icon
Presenting both closing price and trading volume
Icon
Performance comparisons among stocks
Icon
Comparing return versus volatility for several stocks
Icon
Finding manuals, examples, and videos
Icon
Installing the matplotlib module independently
Icon
Summary
Icon
Exercises
8
8. Statistical Analysis of Time Series
Icon
8. Statistical Analysis of Time Series
Icon
Installing Pandas and statsmodels
Icon
Using Pandas and statsmodels
Icon
Open data sources
Icon
Retrieving data to our programs
Icon
Several important functionalities
Icon
Return estimation
Icon
Merging datasets by date
Icon
T-test and F-test
Icon
Many useful applications
Icon
Constructing an efficient frontier
Icon
Understanding the interpolation technique
Icon
Outputting data to external files
Icon
Python for high-frequency data
Icon
More on using Spyder
Icon
A useful dataset
Icon
Summary
Icon
Exercise
9
9. The Black-Scholes-Merton Option Model
Icon
9. The Black-Scholes-Merton Option Model
Icon
Payoff and profit/loss functions for the call and put options
Icon
European versus American options
Icon
Cash flows, types of options, a right, and an obligation
Icon
Normal distribution, standard normal distribution, and cumulative standard normal distribution
Icon
The Black-Scholes-Merton option model on non-dividend paying stocks
Icon
The p4f module for options
Icon
European options with known dividends
Icon
Various trading strategies
Icon
Relationship between input values and option values
Icon
Greek letters for options
Icon
The put-call parity and its graphical representation
Icon
Binomial tree (the CRR method) and its graphical representation
Icon
Hedging strategies
Icon
Summary
Icon
Exercises
10
10. Python Loops and Implied Volatility
Icon
10. Python Loops and Implied Volatility
Icon
Definition of an implied volatility
Icon
Understanding a for loop
Icon
Estimation of IRR via a for loop
Icon
Understanding a while loop
Icon
Using keyboard commands to stop an infinitive loop
Icon
Estimating implied volatility by using an American call
Icon
Measuring efficiency by time spent in finishing a program
Icon
The mechanism of a binary search
Icon
Sequential versus random access
Icon
Looping through an array/DataFrame
Icon
Retrieving option data from CBOE
Icon
Retrieving option data from Yahoo! Finance
Icon
The put-call ratio
Icon
Summary
Icon
Exercises
11
11. Monte Carlo Simulation and Options
Icon
11. Monte Carlo Simulation and Options
Icon
Generating random numbers from a standard normal distribution
Icon
Generating random numbers from a uniform distribution
Icon
Using simulation to estimate the pi value
Icon
Generating random numbers from a Poisson distribution
Icon
Bootstrapping with/without replacements
Icon
Distribution of annual returns
Icon
Simulation of stock price movements
Icon
Finding an efficient portfolio and frontier
Icon
Geometric versus arithmetic mean
Icon
Long-term return forecasting
Icon
Pricing a call using simulation
Icon
Exotic options
Icon
Barrier in-and-out parity
Icon
Pricing lookback options with floating strikes
Icon
Using the Sobol sequence to improve the efficiency
Icon
Summary
Icon
Exercises
12
12. Volatility Measures and GARCH
chevron up
Icon
12. Volatility Measures and GARCH
Icon
Conventional volatility measure – standard deviation
Icon
Tests of normality
Icon
Lower partial standard deviation
Icon
Test of equivalency of volatility over two periods
Icon
Test of heteroskedasticity, Breusch, and Pagan (1979)
Icon
Retrieving option data from Yahoo! Finance
Icon
Volatility smile and skewness
Icon
The ARCH model
Icon
The GARCH (Generalized ARCH) model
Icon
Summary
Icon
Exercises
13
Index
Icon
Index

The GARCH (Generalized ARCH) model

Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) is an important extension of ARCH, by Bollerslev (1986). The GARCH (p,q) process is defined as follows:

The GARCH (Generalized ARCH) model

Here, The GARCH (Generalized ARCH) model is the variance at time t, q is the order for the error terms, p is the order for the variance, The GARCH (Generalized ARCH) model is a constant, The GARCH (Generalized ARCH) model is the coefficient for the error term at t-i, The GARCH (Generalized ARCH) model is the coefficient for the variance at time t-i. Obviously, the simplest GARCH process is when both p and q are set to 1, that is, GARCH (1,1), which has following formula:

The GARCH (Generalized ARCH) model

Simulating a GARCH process

Based on the previous program related to ARCH (1), we could simulate a GARCH (1,1) process as follows:

import scipy as sp
sp.random.seed(12345)
n=1000          # n is the number of observations
n1=100          # we need to drop the first several observations
n2=n+n1         # sum of two numbers
alpha=(0.1,0.3)     # GARCH (1,1) coefficients alpha0 and alpha1, see Equation (3)
beta=0.2
errors=sp.random.normal(0,1,n2)
t=sp.zeros(n2...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
Start a 7-day FREE trial
End of Section 10
Next Section

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY